package com.starhub.application.rag.service.impl;

import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.data.document.Metadata;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingMatch;
import dev.langchain4j.store.embedding.EmbeddingSearchResult;
import dev.langchain4j.store.embedding.filter.Filter;
import dev.langchain4j.store.embedding.filter.MetadataFilterBuilder;

import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Future;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;
import java.util.stream.Collectors;
import java.util.concurrent.LinkedBlockingQueue;
import jakarta.annotation.PreDestroy;
import lombok.extern.slf4j.Slf4j;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import com.starhub.application.collection.entity.KnowledgeBase;
import com.starhub.application.collection.entity.KnowledgeCollection;
import com.starhub.application.collection.enums.MetaDataCollectionTypeEnums;
import com.starhub.application.collection.service.DataDefinitionService;
import com.starhub.application.collection.service.DataManipulationService;
import com.starhub.application.collection.service.KnowledgeBaseService;
import com.starhub.application.collection.service.KnowledgeCollectionService;
import com.starhub.application.interfaces.model.ModelConfigService;
import com.starhub.application.model.biz.VectorModelFactory;
import com.starhub.application.model.enums.VectorModelTypeEnum;
import com.starhub.application.model.util.DimensionUtil;
import com.starhub.application.rag.constant.RagConstant;
import com.starhub.application.rag.dto.MetaRagDto;
import com.starhub.application.rag.dto.RetrieveDto;
import com.starhub.application.rag.enums.MetaDataTypeEnums;
import com.starhub.application.rag.processor.domain.EmbeddingConditionProcessor;
import com.starhub.application.rag.processor.domain.EmbeddingMetaProcessor;
import com.starhub.application.rag.result.RagResult;
import com.starhub.application.rag.result.RetrieveResult;
import com.starhub.application.rag.service.EmbeddingService;
import com.starhub.application.rag.service.RagService;
import com.starhub.application.rag.store.MilvusStore;
import com.starhub.application.rag.store.MilvusStore.SimilarityResult;
import com.starhub.application.rag.store.RetrieveStore;
import com.starhub.application.rag.transformer.SimpleTextSegmentTransformer;
import com.starhub.application.rag.util.MilvusConditionBuilder;
import com.starhub.common.bean.model.ModelConfig;
import com.starhub.integration.milvus.langchain.MilvusMetadataFilterMapper;
import com.starhub.utils.Util;
import com.starhub.application.rag.config.ThreadPoolConfig;

/**
 * RAG服务实现类
 * 
 * @version: V1.0
 * @author: liuhf
 * @time 2025-06-22
 */
@Slf4j
@Service
public class RagServiceImpl implements RagService {

    @Autowired
    private ModelConfigService modelConfigService;

    @Autowired
    SimpleTextSegmentTransformer simpleTextSegmentTransformer;

    @Autowired
    private MilvusStore milvusStore;
    
    @Autowired
    private KnowledgeBaseService knowledgeBaseService;

    @Autowired
    private KnowledgeCollectionService knowledgeCollectionService;

    @Autowired
    private DataManipulationService dataManipulationService;

    @Autowired
    private RetrieveStore retrieveStore;

    @Autowired
    private MilvusConditionBuilder milvusConditionBuilder;

    @Autowired
    private EmbeddingService embeddingService;

    @Autowired
    private ThreadPoolConfig threadPoolConfig;

    @Autowired
    private EmbeddingMetaProcessor embeddingMetaProcessor;

    @Autowired
    private EmbeddingConditionProcessor embeddingConditionProcessor;

    @Autowired
    private DataDefinitionService dataDefinitionService;

    @Override
    public RetrieveDto checkRetrieveDto(RetrieveDto retrieveDto) {

        // 获取目标知识库表
        KnowledgeCollection knowledgeCollection = knowledgeCollectionService.selectById(Util.getLongValue(retrieveDto.getCollectionId()));

        // 获取目标知识库
        KnowledgeBase knowledgeBase = knowledgeBaseService.selectById(Util.getLongValue(knowledgeCollection.getBaseId()));

        // 获取配置
        retrieveDto.setCollectionIdentifier(knowledgeCollection.getCollectionIdentifier());
        retrieveDto.setVectorModel(knowledgeBase.getVectorModel());
        retrieveDto.setDimension(DimensionUtil.getDimension(knowledgeBase.getVectorModel()));

        if(retrieveDto.getMinScore() == null){
            retrieveDto.setMinScore(RagConstant.RETRIEVE_EMBEDDING_MIN_SCORE);
        }
        if(retrieveDto.getTopK() == null){
            retrieveDto.setTopK(RagConstant.RETRIEVE_EMBEDDING_COUNT);
        }
        return retrieveDto;
    }

    /**
     * 根据内容检索知识条目(内容检索模式)
     * 
     * @param RetrieveDto 检索条件
     * @return 检索结果
     */
    @Override
    public RagResult retrieve(RetrieveDto retrieveDto) {
        try {
            return retrieveStore.retrieve(retrieveDto);
        } catch (Exception e) {
            throw new RuntimeException("向量检索失败: " + e.getMessage(), e);
        }
    }

    /**
     * 根据条件检索并创建提示
     * 
     * @param RetrieveDto 检索条件
     * @return 提示对象
     */
    @Override
    public RagResult retrieveByCondition(RetrieveDto retrieveDto) {
        try {
            /*
             * // 1. 构建过滤器
             * Filter filter = new MetadataFilterBuilder(retrieveDto.getQueryMetaKey())
             * .isEqualTo(retrieveDto.getQueryText());
             * 
             * // 2. 获取Milvus配置和向量存储对象
             * MilvusConfig config =
             * milvusConfigService.getConfigByMark(RagConstant.MILVUS_MARK);
             * EmbeddingStore<TextSegment> embeddingStore =
             * langchainStoreBuilder.getOrCreateMilvusEmbeddingStore(config,
             * retrieveDto.getCollectionIdentifier(), retrieveDto.getDimension());
             * 
             * // 3. 执行向量检索
             * EmbeddingSearchRequest searchRequest = EmbeddingSearchRequest.builder()
             * .filter(filter)
             * .build();
             * 
             * EmbeddingSearchResult<TextSegment> searchResult =
             * embeddingStore.search(searchRequest);
             * 
             * RagResultDto ragResult = RagResultDto.builder()
             * .question(retrieveDto.getQueryText())
             * .searchResult(searchResult)
             * .build();
             * return ragResult;
             */
            return null;
        } catch (Exception e) {
            throw new RuntimeException("向量检索失败: " + e.getMessage(), e);
        }
    }

    /**
     * 检查元数据检索条件
     * @param metaRagDto
     * @return
     */
    @Override
    public MetaRagDto checkMetaRagDto(MetaRagDto metaRagDto) {
        // 获取元数据关联设置
        List<Long> metaCollectionIds = dataDefinitionService.getLinks(Util.getLongValue(metaRagDto.getMainCollectionId()));
        metaRagDto.setMetaCollectionIds(metaCollectionIds);
        
        // 默认元数据类型为元数据关联
        metaRagDto.setMetaDataType(MetaDataTypeEnums.METALINK);

        // 获取主数据表信息
        KnowledgeCollection mainCollection = knowledgeCollectionService.selectById(Util.getLongValue(metaRagDto.getMainCollectionId()));
        metaRagDto.setMainCollectionIdentifier(mainCollection.getCollectionIdentifier());
        metaRagDto.setBaseId(Util.null2String(mainCollection.getBaseId())); 
        return metaRagDto;
    }

    /**
     * 根据元数据检索知识条目
     * 
     * @param metaRagDto 检索条件
     * @return 检索结果
     */
    @Override
    public List<TextSegment> retrieveByMetaData(MetaRagDto metaRagDto) {
        try {
            // 1. 获取默认向量模型并对 queryText 进行向量化
            KnowledgeBase knowledgeBase = knowledgeBaseService
                    .selectByIdAndNotDeleted(Util.getLongValue(metaRagDto.getBaseId()));
            if (knowledgeBase == null) {
                throw new IllegalArgumentException("所属知识库不存在");
            }

            // 知识库对应的向量模型
            String vectorModel = knowledgeBase.getVectorModel();
            
            // 对输入内容进行向量化
            Embedding questionEmbedding = embeddingService.embedding(vectorModel, metaRagDto.getQueryText());
            
            // 2. 根据不同的召回模式，执行不同的检索任务，获取检索条件
            String condition = "";
            if(metaRagDto.getMetaDataType() == MetaDataTypeEnums.METALINK){
                switch (metaRagDto.getRetrieveType()) {
                    case EMBEDDING:
                        // 执行向量化检索
                        condition = embeddingMetaProcessor.embeddingRetrievalMeta(metaRagDto, vectorModel, questionEmbedding);
                        break;
                    case AGENT:
                        // 执行智能体检索
                        condition = agentRetrievalCondition(metaRagDto, vectorModel);
                        break;
                }
            }else if(metaRagDto.getMetaDataType() == MetaDataTypeEnums.DATABASE){
                switch (metaRagDto.getRetrieveType()) {
                    case EMBEDDING:
                        // 执行向量化检索
                        condition = embeddingConditionProcessor.embeddingRetrievalCondition(metaRagDto, vectorModel, questionEmbedding);
                        break;
                    case AGENT:
                        // 执行智能体检索
                        condition = agentRetrievalCondition(metaRagDto, vectorModel);
                        break;
                }
            }
            
            // 3. 根据检索条件，执行向量检索
            List<TextSegment> results = new ArrayList<>();
            if (!Util.isEmpty(condition)) {
                List<SimilarityResult> _results = milvusStore.search(questionEmbedding, metaRagDto.getTopK(), metaRagDto.getMinScore(), condition, metaRagDto.getMainCollectionIdentifier());
                results = _results.stream().map(SimilarityResult::getTextSegment).collect(Collectors.toList());
            }

            // 4. 如果检索结果为空，则补偿常规RAG检索
            if(results.isEmpty()) {
                RetrieveDto retrieveDto = RetrieveDto.builder()
                        .queryText(metaRagDto.getQueryText())
                        .vectorModel(vectorModel)
                        .collectionIdentifier(metaRagDto.getMainCollectionId())
                        .dimension(DimensionUtil.getDimension(vectorModel))
                        .topK(metaRagDto.getTopK())
                        .minScore(metaRagDto.getMinScore())
                        .build();   

                EmbeddingSearchResult<TextSegment> searchResult = retrieveStore.retrieve(retrieveDto, questionEmbedding).getSearchResult();
                results = searchResult.matches().stream().map(EmbeddingMatch::embedded).collect(Collectors.toList());
            }

            return results;
        } catch (Exception e) {
            log.error("元数据检索失败: {}", e.getMessage(), e);
            throw new RuntimeException("元数据检索失败: " + e.getMessage(), e);
        }
    }

    /**
     * 智能体检索
     * 
     * @param metaRagDto
     * @param vectorModel
     * @return
     */
    private String agentRetrievalCondition(MetaRagDto metaRagDto, String vectorModel) {
        // TODO 智能体检索
        throw new UnsupportedOperationException("agentRetrieval not implemented");
    }

}
